提出分层交叉注意力网络，提升虚拟试穿真实感
Hierarchical Cross-Attention Network for Virtual Try-On
- 分两阶段建模：几何匹配与试穿，用分层交叉注意力捕捉人衣关联
- 在VTAB、VITON-HD数据集上达最优，视觉真实感显著提升
- 适合做服装电商、数字孪生的开发者参考
本文提出一种创新的虚拟试穿解决方案——分层交叉注意力网络（HCANet）。该网络包含几何匹配与试穿两个核心阶段，均引入新型分层交叉注意力（HCA）模块，有效捕捉人体与服装之间的长距离跨模态相关性。通过层次化设计，实现对人衣交互关系的精细建模，保留关键细节以增强真实感。实验表明，HCANet在定量指标和主观评估中均表现优异，在VTAB与VITON-HD数据集上达到当前最优性能，显著提升了虚拟试穿结果的准确性和视觉真实性，为虚拟试穿技术发展带来重要推进。
原文摘要 · Abstract (English)
In this paper, we present an innovative solution for the challenges of the virtual try-on task: our novel Hierarchical Cross-Attention Network (HCANet). HCANet is crafted with two primary stages: geometric matching and try-on, each playing a crucial role in delivering realistic virtual try-on outcomes. A key feature of HCANet is the incorporation of a novel Hierarchical Cross-Attention (HCA) block into both stages, enabling the effective capture of long-range correlations between individual and clothing modalities. The HCA block enhances the depth and robustness of the network. By adopting a hierarchical approach, it facilitates a nuanced representation of the interaction between the person and clothing, capturing intricate details essential for an authentic virtual try-on experience. Our experiments establish the prowess of HCANet. The results showcase its performance across both quantitative metrics and subjective evaluations of visual realism. HCANet stands out as a state-of-the-art solution, demonstrating its capability to generate virtual try-on results that excel in accuracy and realism. This marks a significant step in advancing virtual try-on technologies.
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